Cross-Platform TinyML Bytecode Execution on Edge Devices
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Solution Overview
Problem
Edge computing devices in IoT systems face challenges due to limited memory and computational power, leading to inefficiencies in data processing and security concerns, exacerbated by platform fragmentation, lack of interoperability, and data privacy issues, as they often rely on centralized cloud processing and lack common technical standards.
Innovation Solution
The development of cross-platform applications using TinyContainers, which decouple machine learning models from hardware, enabling local processing on edge devices through containerization, secure bytecode deployment, and adaptive mesh technologies like TinyMesh for resilient and energy-efficient operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If data is sent to centralized cloud service for processing, then computational power is sufficient, but network bandwidth is consumed and data privacy is compromised
Solution Approach 1:
The system segments data processing by deploying lightweight machine learning models directly on edge devices, separating computational tasks from centralized cloud services. This allows processing to occur locally at the data source, eliminating the need to transmit raw data to the cloud while maintaining sufficient computational capability through distributed intelligence.
Solution Approach 2:
The patent introduces an intermediary layer of edge computing devices that perform initial data processing and filtering before any data reaches the cloud. These edge devices act as mediators between data sources and centralized services, processing data locally to reduce network bandwidth consumption while preserving essential information for cloud analytics.
2Productivity
If applications are developed for specific IoT platforms, then platform-specific performance is optimized, but interoperability across different devices is reduced
Solution Approach 1:
The patent implements universal interoperability through standardized communication protocols and abstracted hardware interfaces that allow the same application code to run across diverse IoT platforms. The system provides multi-functionality by enabling a single application framework to operate on different hardware architectures (ARM, x86, RISC-V) and operating systems (Linux, Android, embedded RTOS) without requiring platform-specific reimplementation.
3Use of energy by moving object
If edge devices have limited memory and computational power, then device size and power consumption are reduced, but data processing capability is constrained
Solution Approach 1:
The system applies local quality by deploying appropriately sized machine learning models tailored to the specific computational capabilities of each edge device. Rather than using a one-size-fits-all approach, the patent adapts model complexity, precision, and architecture to match the local hardware constraints of each device, optimizing the balance between processing capability and power consumption for each specific platform.
Solution Approach 2:
The patent implements dynamic resource allocation where edge devices can adjust their computational workload dynamically based on available power and memory resources. The system can migrate processing tasks between edge devices and cloud services based on real-time resource availability, allowing devices to operate at optimal performance points that adapt to changing hardware conditions and power constraints.
Data Source
AI summary
The technology disclosed relates to systems and methods of cross-platform programming of tiny machine learning (ML) applications. The method includes providing a first declarative instruction that, when processed, interacts with a cross-platform capability of tiny ML hardware. The method includes providing a second declarative instruction that, when processed, invokes at least one procedure block. The method includes providing a third declarative instruction that, when processed, causes output from the tiny ML hardware. The method includes compiling the ML procedure block and the tiny ML model into bytecode. The bytecode interacts, via a virtual machine (VM) layer, with the capability to produce the output responsive to the first, second and third declarative instructions. The method includes executing the bytecode on the tiny ML hardware to process the declarative instructions without recompilation of the bytecode to adapt to alternative instances of the tiny ML hardware, each running its alternative VM layer.